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    <titleInfo>
        <title>Deriving Boolean structures from distributional vectors</title>
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    <name type="personal">
        <namePart type="given">German</namePart>
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        <namePart type="given">Denis</namePart>
        <namePart type="family">Paperno</namePart>
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    <name type="personal">
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    <abstract>Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00145</identifier>
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        <date>2015</date>
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%0 Journal Article
%T Deriving Boolean structures from distributional vectors
%A Kruszewski, German
%A Paperno, Denis
%A Baroni, Marco
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F kruszewski-etal-2015-deriving
%X Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.
%R 10.1162/tacl_a_00145
%U https://aclanthology.org/Q15-1027
%U https://doi.org/10.1162/tacl_a_00145
%P 375-388

Markdown (Informal)

[Deriving Boolean structures from distributional vectors](https://aclanthology.org/Q15-1027) (Kruszewski et al., TACL 2015)

• Deriving Boolean structures from distributional vectors (Kruszewski et al., TACL 2015)

ACL

• German Kruszewski, Denis Paperno, and Marco Baroni. 2015. Deriving Boolean structures from distributional vectors. Transactions of the Association for Computational Linguistics, 3:375–388.